Data augmentation for improving the performance of deep neural networks has been an important topic in the field of music information retrieval (MIR) in recent years. Schluter and Grill [1]} were one of the earliest to systematically explore the utility of music data augmentation for singing voice detection with neural networks. They found pitch shifting combined with time stretching and random frequency filtering to be quite helpful in reducing the classification error.
Uhlich et al. [2]} proposed two neural network architectures capable of yielding state-of-the-art results for music source separation at that time, and further boosted the performance through data augmentation and network blending. Hawthorne et al. [3]} experiments with the use of mixing techniques such as equalization, contrast, and reverberation in an attempt to make their automatic piano transcription model more robust to different recording environments and piano qualities. To our knowledge, such mixing techniques have not been employed in existing work on source separation.
